Image processing is a process by which an imaging application alters input image data. For example, image processing may be used to change the color space of a digital image. Image processing may be implemented in conjunction with a printing device in order to adjust the color appearance or perceived sharpness of an image according to the specifications of the printing device. Often, an image includes noise artifacts that reduce image quality of the image.
Sharpening noisy images typically results in unwanted noise enhancement. Furthermore, many typical denoising techniques are based on the extent of noise in the image. Noise estimation techniques are used to estimate image noise. A major problem with noise estimation in natural images is differentiating between noisy regions and textured regions. Currently, there are several methods of image noise estimation. However, the current available methods each present certain operational drawbacks or limitations.
One current method estimates noise within the context of an image sharpening system by the standard deviation of unsharp mask values in pixels with low local gradients. However, computations of pixel gradients and averages are intensive. In particular, these computations employ two-dimensional filters over the full image. Since this method is computationally intensive, a substantial contribution of computing resources is required, thereby providing limited applicability.
Other current methods for noise estimation include analysis of the distribution of the local gradient amplitude, analysis of the power spectrum of the image in order to estimate the variance of additive white noise, analysis of the mode relating to the smallest gradient value in a bivariate histogram of smoothed gradient and smooth luminance. Similar to the above described method, these methods are computationally intensive, and therefore provide inefficient noise estimation.